The present invention is concerned with the field of automatic speech recognition (ASR). More specifically, the present invention is concerned with methods and apparatus for calculating language model look ahead probabilities.
A language model LM, which is independent of acoustic observations, is used in ASR to incorporate restrictions on how words in a language should be concatenated together to form sentences. Generally language models are used which are based on n-grams where n−1 history words are considered. As the language model requires complete words for it to be applied, language model look ahead LMLA probabilities were introduced which allows the language model to start to be applied before a word is completed.
Language model look-ahead (LMLA) can accelerate the n-gram decoding process. The main idea of LMLA is using LMLA probability as linguistic score when the current word id is unknown. This technology leads to more efficient pruning in the decoding process. However, the calculation cost of generating the LMLA probabilities is high in traditional method. When a high order LMLA, for example trigram LMLA is adopted, the number of different trigram contexts that occur in the search space increase dramatically compared to the number of bigram contexts. As a result, the calculation cost of trigram LMLA is much higher than the bigram LMLA and it is even can not be compensated by the search space reduction from the use of this technology. In order to overcome this problem, some good methods have been presented, including node based LMLA probability cache, pre-calculating the LM probabilities and perfect hashing. Most these methods focus on how to cache and look up the LMLA probabilities efficiently. However, generating the LMLA probability itself is a time consuming process.